Multi-Session Parcellation of the Human Brain Using Resting-State fMRI

Abstract

The parcellation of human brain is important to discover the neural mechanisms behind human behavior. Despite many statistical models are proposed to compute parcellation using resting-state functional magnetic resonance imaging (rs-fMRI), there still remains challenges for obtaining more reliable parcellations for individuals. To address these challenges, we design a multi-session parcellation approach based on data of several sessions for each subject. Initially, we cluster original brain data into a certain number of homogeneous supervoxels to reduce computational cost of the subsequent stages. Secondly, we use spectral clustering method to cluster our supervoxels into several ROIs to obtain a reasonable parcellation of each subject. Thirdly, we propose a method based on adjacency matrix to merge parcellations from different sessions to obtain a more reproductive parcellation of each subject. The performance of the proposed algorithm is evaluated with two commonly used metrics, including silhouette width and Dice's coefficient. The experiments demonstrate the effectiveness of the proposed approach with a better reproducibility. The proposed algorithm has high potential to generate reliable human brain parcellations for analyzing individual brain network more reliably.